1. Field of the Invention
The invention relates to an image processing method, and more particularly, to a hierarchical motion deblurring method for a single image.
2. Description of Related Art
Motion blur is caused by relative motion between the camera and the scene within the exposure time period. This problem frequently occurs when taking photographs under low-light conditions using a hand-held camera. Another type of motion blur is caused by a moving object captured with a static camera.
For images degraded by the motion blur, restoring the images is a long-standing research problem in computer vision and image processing. Currently, a number of algorithms have been proposed to tackle this problem and they can be roughly classified into three groups: single-image deblurring, multiple-image deblurring, and computational photography.
Herein, the real camera motion is usually too complicated to estimate from a blurred image when it involves camera rotation or large scene depth variations. To simplify the problem formulation, previous researches usually assumed the camera motion to be perpendicular to the optical axes and the effect of scene depth variation can be neglected. In other words, the blur kernel, or named point spread function (PSF), is assumed to be spatially invariant. Under this assumption, a blurred image B, can be modeled as the convolution of the clear image I, and the blur kernel, f, as given in the following:B=If+n  (1)
where n is the additive noise.
If both I and f are unknowns, to solve equation (1) is a blind deconvolution problem. Even with the spatially-invariant assumption with the kernel, the problem is still an ill-posed one, because I and f are highly under-constrained and there are many possible combinations of I and f that can be convolved to be equal to the blurred image B.